Unstructured databases are becoming a popular alternative to conventional relational databases due to the relaxed format for data storage and the wider range of data structures that may be stored. In contrast to conventional relational databases, where strong typing imposes data constraints to adhere to a predetermined row and column format, unstructured databases impose no such restrictions.
Unstructured databases have no formal field or record structure, and may be more accurately characterized as a collection of facts. Unlike their structured counterparts, typically a SQL (Structured Query Language) database, which denotes data in fixed length fields enumerated in records in a tabular form, an unstructured database labels fields for storing values in a document. A set of documents defines a collection, in which the documents in a collection may share some, none, or all of a particular field. Due to the unstructured nature, however, conventional analytical approaches for generating and analyzing trends may not lend themselves well to an unstructured database. Since there are effectively no formal bounds or range, it can be difficult to analyze or extract conclusory results, due to computational intensity or complexity. Queries directed to unstructured databases may invoke substantial computations resources in traversing a volume of unstructured data in an effort to satisfy the requested query.